We investigate techniques for supervised domain adaptation for neural machine
translation where an existing model trained on a large out-of-domain dataset is
adapted to a small in-domain dataset. In this scenario, overfitting is a major
challenge. We investigate a number of techniques to reduce overfitting and
improve transfer learning, including regularization techniques such as dropout
and L2-regularization towards an out-of-domain prior. In addition, we introduce
tuneout, a novel regularization technique inspired by dropout. We apply these
techniques, alone and in combination, to neural machine translation, obtaining
improvements on IWSLT datasets for English->German and English->Russian. We
also investigate the amounts of in-domain training data needed for domain
adaptation in NMT, and find a logarithmic relationship between the amount of
training data and gain in BLEU score.

本研究探讨了神经机器翻译的监督域自适应技术，针对现有模型在大型非领域数据集训练后，如何适应小型领域数据集所出现的过拟合问题。在整个过程中，正则化技术，如 dropout 和 L2 正则化与非领域先验之间的关系得到了深入研究。此外，文章提出了一种新型的正则化技术 ——tuneout，即启发式 dropout。我们将这些技术单独或结合应用于神经机器翻译，以英语 -> 德语、英语 -> 俄语的 IWSLT 数据集为例取得了改进。此外，我们还研究了 NMT 领域中所需的领域内训练数据量，并发现培训数据与 BLEU 分数之间存在对数关系。